Employment of physical programming as the base method to develop an optimization approach for obtaining a tradeoff solution for multi-objective nonlinear perturbed rendezvous was investigated. The physical programming...
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Employment of physical programming as the base method to develop an optimization approach for obtaining a tradeoff solution for multi-objective nonlinear perturbed rendezvous was investigated. The physical programming was developed by Messac initially as an effective multi-objective and design optimization way successfully applied in various areas of engineering and research operations. This design approach incorporated three objectives apart from consideration of trajectory perturbations with minimum propellant cost, minimum time of flight, and maximum trajectory safety. It was found that designer preferred solution, can be directly applied to a mission plan. Successful demonstration of the physical programming method for efficient generation of desired design solution of perturbed rendezvous trajectory problem with homing example. Its beneficial to apply physical programming method with heuristic optimization algorithms to complex spacecraft trajectory design problems.
A method for transonic compressor multi-objective design optimization was developed and applied to the NASA rotor 37, a test case representative of complex three-dimensional viscous flow structures in transonic bladin...
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A method for transonic compressor multi-objective design optimization was developed and applied to the NASA rotor 37, a test case representative of complex three-dimensional viscous flow structures in transonic bladings. The optimization problem considered was to maximize the isentropic efficiency of the rotor and to maximize its pressure ratio at the design point, using a constraint on the mass flow rate. The three-dimensional Navier-Stokes code CFX-TASCflow(R) was used for the aerodynamic analysis of blade designs. The capability of the code was validated by comparing the computed results to experimental data available in the open literature from probe traverses up-and downstream or the rotor. A multi-objectiveevolutionary algorithm was used for handling the optimization problem that makes use of Pareto optimality concepts and implements a novel genetic diversity evaluation method to establish a criterion for fitness assignment. The optimal rotor configurations, which correspond to the maximum pressure ratio and maximum efficiency, were obtained and compared to the original design.
An effective strategy for airfoil numerical optimizations is presented that deals with multi-objective and multipoint problems and is specifically designed for helicopter applications. This technique is tested on seve...
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An effective strategy for airfoil numerical optimizations is presented that deals with multi-objective and multipoint problems and is specifically designed for helicopter applications. This technique is tested on several realistic problems of airfoil optimization with the aim of improving their aerodynamic performance by searching for optimal shape. The procedure is based On a multi-objective surrogate-assisted memetic algorithm coupled to a Navier-Stokes solver. First, the peculiar features of the algorithm are described with particular attention to its advantages when compared with more traditional evolutionary or gradient-based algorithms. Finally, the results of the optimizations carried out using different operating conditions are presented;starting from the optimal Pareto fronts, several solutions are selected and compared in terms of shapes and performance.
A study is conducted to demonstrate constraint handling and multi-objective methods for the evolution of interplanetary trajectories. The study is based on the methods developed during GTOC6 and few advanced evolution...
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A study is conducted to demonstrate constraint handling and multi-objective methods for the evolution of interplanetary trajectories. The study is based on the methods developed during GTOC6 and few advanced evolutionary techniques able to evolve complex Jupiter capture options are proposed. The evolutionary process is set up to be able to deal with constraints, along with multiple objectives and show the use of advanced evolutionary techniques to effectively search the solution space in both cases. The investigations show how these techniques allow the evolution of solutions of use to trajectory designers for the complex trajectory case considered in this study where other approaches fail to find acceptable solutions in a reasonable time.
In multigravity-assist trajectory optimization, the size of the design space is a variable itself. The objective functions are usually replete with local minima. This paper presents a multi-objective hidden genes gene...
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In multigravity-assist trajectory optimization, the size of the design space is a variable itself. The objective functions are usually replete with local minima. This paper presents a multi-objective hidden genes genetic algorithm (MOHGGA) for trajectory optimization. The length of the chromosome is selected large enough to enable modeling a given maximum number of swing-bys and maximum number of deep space maneuvers (DSMs). Binary tags are appended to those genes that control the swing-bys and DSMs. These binary tags are used to remove/add swing-bys and DSMs to a trajectory solution, and hence enable optimization among solutions of different sizes (different topologies). The MOHGGA generates Pareto fronts that have solutions of, in general, different number of swing-bys, swing-by planets, launch and arrival dates, and number of DSMs. Two objectives are considered in this paper: the total mission cost and total time of flight. An elitist nondominated sorting genetic algorithm is used. Local optimization is conducted on one objective function, holding the other objective function constant, to further improve the resulting Pareto front. Numerical results of four benchmark test cases for missions to Mars, Jupiter, Saturn, and Mercury are presented. The results demonstrate the capability of MOHGGA in searching for optimal trajectory topologies while optimizing two objectives.
Real-world operational use of parallel multi-objectiveevolutionaryalgorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heteroge...
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Real-world operational use of parallel multi-objectiveevolutionaryalgorithms requires successful searches in constrained wall-clock periods, limited trial-and-error algorithmic analysis, and scalable use of heterogeneous computing hardware. This study provides a cross-disciplinary collaborative effort to assess and adapt parallel multi-objectiveevolutionaryalgorithms for operational use in satellite constellation design using large dedicated clusters with heterogeneous processor speeds/architectures. A statistical, metric-based evaluation framework is used to demonstrate how time-continuation, asynchronous evolution, dynamic population sizing, and epsilon dominance archiving can be used to enhance both simple master-slave parallelization strategies and more complex multiple-population schemes. Results for a benchmark constellation design coverage problem show that simple master-slave schemes that exploit time-continuation are often sufficient and potentially superior to complex multiple-population schemes.
In this work, a stability-constrained biobjective (aerodynamic drag and heating) design optimization for the hypersonic spiked bodies is conducted. The shape optimization includes both the forebody shape and the spike...
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In this work, a stability-constrained biobjective (aerodynamic drag and heating) design optimization for the hypersonic spiked bodies is conducted. The shape optimization includes both the forebody shape and the spike/aerodisk shape through a generic parametric representation of the geometry. The NSGA-II multi-objective algorithm is coupled with the kriging surrogates that are constructed based on numerical solutions of laminar VISCOUS flows around the geometries. The optimization reveals that the two objectives, minimum drag and minimum heating, are competing for spiked forebodies such that a set of Pareto front solutions are obtained. On the minimum-drag and maximum-aeroheating extreme, the optimal designs are characterized by highly blunt, nearly flat forebodies with the possibility of flow unsteadiness. On the other extreme, the optimal designs are commonly of slender forebodies with small bluntness. Between the two extremes, a variety of nondominant designs can be found.
multi-objectiveevolutionaryalgorithms have been shown to be effective optimization tools to search the complex tradeoff spaces of satellite constellation design. Often, the metrics that make up the design tradeoff r...
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multi-objectiveevolutionaryalgorithms have been shown to be effective optimization tools to search the complex tradeoff spaces of satellite constellation design. Often, the metrics that make up the design tradeoff require lengthy function evaluation time, resulting in a decreased utility of serial multi-objectiveevolutionaryalgorithms. In this research, the authors implement two parallel processing multi-objectiveevolutionary algorithm paradigms, the master-slave and island models, on a heterogeneous system of processors and operating systems. The efficiency and effectiveness of each approach is studied in the context of a regional coverage design problem. The island scheme outperforms the master-slave model with respect to efficiency. A study of the search dynamics for each paradigm demonstrates that both reliably meet the goals of multi-objective optimization (progressing toward the Pareto-optimal front while maintaining a diverse set of solutions). A key conclusion of this research is that both paradigms provide excellent approximations of the true Pareto frontier using a single seed, and when combined across multiple trial runs, they find nearly the entire set of Pareto-optimal solutions.
In this study, a new optimization approach for robust design, design for multi-objective six sigma, has been developed and applied to three robust optimization problems. The design for multi-objective six sigma builds...
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In this study, a new optimization approach for robust design, design for multi-objective six sigma, has been developed and applied to three robust optimization problems. The design for multi-objective six sigma builds on the ideas of design for six sigma, coupled with multiobjectiveevolutionary algorithm, for an enhanced capability to reveal tradeoff information considering both optimality and robustness of design. While design for six sigma requires careful input parameter setting, design for multi-objective six sigma needs no such prior tuning, plus it can reveal the tradeoff information in a single optimization run. Three robust optimization problems were taken as to demonstrate the capabilities of design for multiobjective six sigma. Results indicate that design for multi-objective six sigma has a more practical and more efficient capability than the design for six sigma to reveal tradeoff design information considering both optimality and robustness of design.
Synthetic biology is reaching the situation where tuning devices by hand is no longer possible due to the complexity of the biological circuits being designed. Thus, mathematical models need to be used in order, not o...
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